A New Dataset and Comparative Study for Aphid Cluster Detection
Aphids are one of the main threats to crops, rural families, and global food security. Chemical pest control is a necessary component of crop production for maximizing yields, however, it is unnecessary to apply the chemical approaches to the entire fields in consideration of the environmental pollution and the cost. Thus, accurately localizing the aphid and estimating the infestation level is crucial to the precise local application of pesticides. Aphid detection is very challenging as each individual aphid is really small and all aphids are crowded together as clusters. In this paper, we propose to estimate the infection level by detecting aphid clusters. We have taken millions of images in the sorghum fields, manually selected 5,447 images that contain aphids, and annotated each aphid cluster in the image. To use these images for machine learning models, we crop the images into patches and created a labeled dataset with over 151,000 image patches. Then, we implement and compare the performance of four state-of-the-art object detection models.
Code (0)
등록된 구현이 없습니다.
Tasks
object-detectionObject DetectionSimilar Papers 제목 키워드 기반
A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields
Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address th…
GPUobject-detectionObject DetectionReal-Time Semantic Segmentation+2Aphid Cluster Recognition and Detection in the Wild Using Deep Learning Models
Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both …
object-detectionObject DetectionOn the Real-Time Semantic Segmentation of Aphid Clusters in the Wild
Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, …
BenchmarkingReal-Time Semantic SegmentationSegmentationSemantic SegmentationDeveloping a Hybrid Convolutional Neural Network for Automatic Aphid Counting in Sugar Beet Fields
Aphids can cause direct damage and indirect virus transmission to crops. Timely monitoring and control of their populations are thus critical. However, the manual counting of aphids, which is the most common practice, is…
Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions
The current vision-based aphid counting methods in water traps suffer from undercounts caused by occlusions and low visibility arising from dense aggregation of insects and other objects. To address this problem, we prop…
object-detectionObject DetectionSmall Object Detection